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Live Inference and Benchmark CT-scan Data with OpenVINO™

Kidney Segmentation with PyTorch Lightning and OpenVINO - Part 4

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kidney segmentation animation

Notebook Contents

This tutorial is a part of the series that shows how to train, optimize, quantize and show live inference on a segmentation model with PyTorch Lightning and OpenVINO.

This is the final part that shows benchmarking results and live inference on the quantized segmentation model. You will see real-time segmentation of kidney CT scans running on a CPU, iGPU, or combining both devices for higher throughput. The processed frames are 3D scans that are shown as individual slices. The visualization slides through the slices with detected kidneys overlayed in red. A pre-trained and quantized model is provided, so running the previous tutorials in the series is not required.

Installation Instructions

For a minimum installation:

  • Create a virtual environment, with python -m venv openvino_env (on Linux you may need to type python3) and activate it with openvino_env\Scripts\activate on Windows or source openvino_env/bin/activate on macOS or Linux.
  • Clone the openvino_notebooks repository: git clone https://github.com/openvinotoolkit/openvino_notebooks/
  • Change to the directory: cd openvino_notebooks
  • Check out the live_inference branch git checkout live_inference
  • Change to the notebook directory: cd notebooks/210-ct-scan-live-inference
  • Install the requirements with pip install --upgrade pip && pip install -r requirements.txt.
  • Run the notebook by typing jupyter lab and doubleclicking on the notebook from the left sidebar.

NOTE: This notebook needs an Internet connection to download data. If you use a proxy server, enable the proxy server in the terminal before typing jupyter lab.

NOTE: To use other notebooks in the openvino_notebooks repository, follow the instructions in the Installation Guide

All notebooks in this series: